A New Approach for the Development of Fast-analysis Proxies for Petroleum Reservoir Simulation

Abstract

The bottleneck of all processes that are using field-scale numerical simulators is the computationally expensive objective function evaluation. Hence, always a gap exists between simulation runs and real-time processing. In this study, a new approach is presented that uses online-adaptive artificial neural networks to develop proxies that mimic the behavior of the actual reservoir simulator. In this approach, initially Latin hypercube sampling is used and then an intelligent sample selection algorithm is developed to improve the online network prediction. The cited approach improves the surrogate model development in two directions. First, proxies can be used while they are developing and, second, samples are selected intelligently and this reduces computational cost.